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We investigate aspects of tram-network section reliability, which operates as a part of the model of whole city tram-network reliability. Here, one of the main points of interest is the character of the chronological development of the disturbances (namely the differences between time of departure provided in schedule and real time of departure) on subsequent sections during tram line operation. These developments were observed in comprehensive measurements done in Krakow, during one of the main transportation nodes (Rondo Mogilskie) rebuilding. All taken building activities cause big disturbances in tram lines operation with effects extended to neighboring sections. In a second part, the stochastic character of section running time will be analyzed more detailed. There will be taken into consideration sections with only one beginning stop and also with two or three beginning stops located at different streets at an intersection. Possibility of adding results from sections with two beginning stops to one set will be checked with suitable statistical tests which are used to compare the means of the two samples. Section running time may depend on the value of gap between two following trams and from the value of deviation from schedule. This dependence will be described by a multi regression formula. The main measurements were done in the city center of Krakow in two stages: before and after big changes in tramway infrastructure.

MODEL OF TRAM LINE OPERATION
(2006)

From passenger's perspective punctuality is one of the most important features of trams operations. Unfortunately in most cases this feature is only insufficiently fulfilled. In this paper we present a simulation model for trams operation with special focus on punctuality. The aim is to get a helpful tool for designing time-tables and for analyzing the effects by changing priorities for trams in traffic lights respectively the kind of track separation. A realization of trams operations is assumed to be a sequence of running times between successive stops and times spent by tram at the stops. In this paper the running time is modeled by the sum of its mean value and a zero-mean random variable. With the help of multiple regression we find out that the average running time is a function depending on the length of the sections and the number of intersections. The random component is modeled by a sum of two independent zero-mean random variables. One of these variables describes the disturbance caused by the process of waiting at an intersection and the other the disturbance caused by the process of driving. The time spent at a stop is assumed to be a random variable, too. Its distribution is estimated from given measurements of these stop times for different tram lines in Kraków. Finally a special case of the introduced model is considered and numerical results are presented. This paper is involved with CIVITAS-CARAVEL project: "Clean and better transport in cites". The project has received research funding from the Community's Sixth Framework Programme. The paper reflects only the author's views and the Community is not liable for any use that may be made of the information contained therein.

From passenger’s perspective, punctuality is one of the most important features of tram route operation. We present a stochastic simulation model with special focus on determining important factors of influence. The statistical analysis bases on large samples (sample size is nearly 2000) accumulated from comprehensive measurements on eight tram routes in Cracow. For the simulation, we are not only interested in average values but also in stochastic characteristics like the variance and other properties of the distribution. A realization of trams operations is assumed to be a sequence of running times between successive stops and times spent by tram at the stops divided in passengers alighting and boarding times and times waiting for possibility of departure . The running time depends on the kind of track separation including the priorities in traffic lights, the length of the section and the number of intersections. For every type of section, a linear mixed regression model describes the average running time and its variance as functions of the length of the section and the number of intersections. The regression coefficients are estimated by the iterative re-weighted least square method. Alighting and boarding time mainly depends on type of vehicle, number of passengers alighting and boarding and occupancy of vehicle. For the distribution of the time waiting for possibility of departure suitable distributions like Gamma distribution and Lognormal distribution are fitted.

The ride of the tram along the line, defined by a time-table, consists of the travel time between the subsequent sections and the time spent by tram on the stops. In the paper, statistical data collected in the city of Krakow is presented and evaluated. In polish conditions, for trams the time spent on stops makes up the remarkable amount of 30 % of the total time of tram line operation. Moreover, this time is characterized by large variability. The time spent by tram on a stop consists of alighting and boarding time and time lost by tram on stop after alighting and boarding time ending, but before departure. Alighting and boarding time itself usually depends on the random number of alighting and boarding passengers and also on the number of passengers which are inside the vehicle. However, the time spent by tram on stop after alighting and boarding time ending is an effect of certain random events, mainly because of impossibility of departure from stop, caused by lack of priorities for public transport vehicles. The main focus of the talk lies on the description and the modelling of these effects. This paper is involved with CIVITAS-CARAVEL project: "Clean and better transport in cites". The project has received research funding from the Community's Sixth Framework Programme. The paper reflects only the author's views and the Community is not liable for any use that may be made of the information contained therein.